Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 12, Issue 4, Pages 1037-1046Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2011.2132759
Keywords
Ensemble learning; Gaussian processes; kernel dimension reduction (KDR); pedestrian counting; statistical landscape features (SLFs)
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Funding
- 973 Program [2006CB705506, 2010CB327900]
- National Science Foundation of China [60975044]
- Fudan University Key Laboratory
- State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University [RCS2008007]
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Estimating the number of pedestrians in surveillance images and videos has important applications in intelligent transportation systems. This problem is particularly challenging when the scenes are densely crowded, in which the techniques of tracking a single pedestrian has limited effectiveness. Alternative approaches employ statistical learning algorithms to infer pedestrian counts directly from visual features computed on images or scenes. In this paper, we describe a system for predicting pedestrian counts that significantly extends the utility of those ideas. Our approach incorporates a richer set of features for statistical modeling. While these features give rise to regression problems in a high-dimensional space, we leverage learning techniques to reduce dimensionality while still attaining high accuracy for predicting the number of pedestrians. Empirical results have validated our strategy. Specifically, our system outperforms state-of-the-art methods on standard benchmark tasks by a large margin.
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